‘Big Data’ Microanalysis Improves Ad Yields

The term “big data” seems to be everywhere these days. It was a hot topic even before Edward Snowden’s revelations about the National Security Agency’s data collection program. Advertisers rely on detailed analysis of their customers to improve the effectiveness of their marketing strategies. It’s also one of the ways that businesses with high fixed costs ensure they receive the greatest revenue for their most valuable inventory.

Put another way, big data analytics can help to ensure sellers don’t leave any money on the table when closing a deal, whether it’s for an airline seat or an ad buy.

Unfortunately, TV stations may be selling themselves short with their current ad sales strategies. As Chuck Davenport, a senior manager with Deloitte Consulting, observed,  “If you do the analysis, you will find that your most valuable inventory, which is going to become scarce as time goes on, is being sold a year or more in advance to someone who would pay much more for it.”   

Using a ‘Price Waterfall’

Davenport’s conclusion is based upon applying the “price waterfall” tool he uses to help media companies improve their pricing management. He presented the waterfall at MFM’s Media Finance Focus 2013 conference this spring. I could see a number of heads nodding in agreement with his conclusion after reviewing the analysis.

Davenport explained that the waterfall is two tiered. The first part of the analysis focuses on “selling erosion,” a waterfall that measures the decline that occurs between a company’s rate card CPM — cost per thousand impressions — to the “invoice CPM” as a result of discounts, production and other expenses that are absorbed by the station.

The second waterfall occurs as the invoice CPM declines even further to a “pocket CPM” as the result of resource erosion — the impact of talent and syndication fees, research, PR and other station expenses.

For Davenport, analyzing selling erosion, the first tier of the waterfall, can help to maximize system-wide revenue, by ensuring that the station sells “the right inventory to the right customer at the right time for the right price.” 

Analysis of the second portion of the waterfall will help with resource management and ensure your company serves “the right customer through the right channels, with the right resources and service levels.”

Behavioral Analysis

Understanding customers is fundamental to achieving these two outcomes. “Behavior-based segmentation allows you to differentiate your services founded on the customers’ needs,” Davenport explained

This is where we need to apply that microanalysis of the big data. It’s akin to the way advertisers segment their customers based upon certain characteristics, behaviors, needs or attitudes they share with one another.

Armed with this data, Davenport said a sales team can serve customers more effectively. They ensure they are aligning the services they are selling with the customer’s needs and “determining the capabilities that will serve each customer more effectively.”

This may sound like common sense, but it’s not commonly occurring. As Jordy Luft, media director for New Orleans-based Peter Mayer agency noted in another conference session, “Your ad sales reps are too interested in selling a particular inventory. But if they want my business, they need to be consultants and listen first. Don’t try to sell me on the first meeting.”

Analyzing Resource Allocations

While Luft’s observation about consultative selling is spot on, Davenport’s waterfall analysis reminds us that we can’t allocate the same amount of time to every sale.

He advised, “Your sales team should focus on the customers who contribute most of the revenue and allocate them to the appropriate segments.” He goes on to say this can be accomplished by reviewing sales data that ranks advertisers and agencies based upon the percentage of earned revenue their spending represents.

When this type of analysis is conducted, Davenport said it’s not uncommon to find that while the 80/20 rule holds up with advertisers but not necessarily with agencies. While 17.8% of advertisers represent 80% of revenue from advertisers, 10.5% of agencies represent 80% of ad revenues collected from ad agencies, according to his analysis.

Companies applying these metrics have found it easier to limit the adverse effect of what Davenport described as “cherry pickers” — advertisers buying as much as 90% of their products in peak periods while receiving substantial discounts. In his view, “Small companies that receive discounts on high-demand products should be highly scrutinized. These contracts should be considered as ‘fix or flush’ customers.”

Improving the Bottom Line

The payoff for applying better pricing control with advertisers makes this type of data analytics worth your while. Davenport’s findings indicate that companies can reduce their effective CPM discounts by 10%-25%. This translates to a 5%-10% revenue and profit opportunity per year, since these savings fall directly to the bottom line.

As he summarized for our conference attendees: “The right implementation of a pricing and yield management program can lead to immediate wins and sustainable long-term results.”

‘MoneyBall’ 

TV stations may also be able to apply the big data analysis that radio stations are using to improve ad revenues by shifting commercial breaks within a local program.

Mark Shannon O’Neill, a partner at the consultancy ROI Media Solutions, explained to conference attendees that, “A rating point is really a range, where a .00449 rounds down to a 4 rating and a .00459 rounds up to a 5.” He went on to say that his radio clients have seen that this one-tenth of a point can add as much as 33% more pricing power to their stations, worth anywhere from $75,000 to $150,000.

“The goal is to get your station’s rating growing with the PUMM (persons using measured media) to get a higher AQH (average quarter-hour rating); it’s the shortest path to increased revenue.”

O’Neill’s examples focused on how moving the break to another time within a local program that may have a slightly different rating, allows the station to maintain control of its overall ad inventory.

While the analysis for radio relies heavily on comparing Arbitron’s portable people meter system with Miller Kaplan revenue reports for radio station markets, it would seem that TV stations could apply similar analytical data to achieve a similar result.

When longtime radio industry reporter and columnist Tom Taylor learned about O’Neill’s use of data to improve ad pricing he described it as “Moneyball for Radio,” citing its similarity to how the Oakland A’s used analysis of player statistics to improve its performance.

If you were unable to attend this year’s Media Finance Focus 2013 and would like to learn more, a summary of our sessions appears in the July-August issue of our member magazine, The Financial Manager, which is currently available on MFM’s website.

As we’ve seen in baseball and across many industry segments, microanalysis of big data can make a big difference in how the (selling) season plays out. I hope these insights are helpful in improving your station’s game.

Since MFM’s mission is to share knowledge and best practices across the media industry, I hope you’ll consider sharing your batting tips with TVNewsCheck readers in the comments section below or on MFM’s LinkedIn discussion forum.

Mary M. Collins is president and CEO of the Media Financial Management Association and its BCCA subsidiary. She can be reached at [email protected]. Her column appears in TVNewsCheck every other week. You can read her earlier columns here.